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migsr22/README.md

Miguel Rocha

Solutions Architect at Google | AI/ML Expert | Cloud Architecture

Website LinkedIn Kaggle Email

Random Dev Quote

πŸ‘¨β€πŸ’» About Me

class MiguelRocha:
    def __init__(self):
        self.role = "Solutions Architect @ Google"
        self.location = "Seattle, WA"
        self.work_experience = 6
        self.education = {
            "masters": "M.S. Business Analytics (UT Dallas)",
            "bachelors": "B.S. Information Technology (UT Dallas)"
        }
        self.interests = ["Generative AI", "Machine Learning", "Cloud Architecture"]
        
    def current_focus(self):
        return [
            "Large Language Models (LLMs)",
            "RAG Implementations",
            "Multimodal AI Solutions",
            "Enterprise Cloud Architecture"
        ]

πŸš€ Professional Journey

Company Role Period
Google Solutions Architect - Generative AI 2024 - Present
Microsoft Applied Scientist - Cloud & AI 2021 - 2023
IBM Data Scientist/ML Engineer 2019 - 2021
Toyota Data Scientist 2018 - 2019

πŸ› οΈ Tech Stack

Click to expand!

Languages & Frameworks

Python SQL PySpark Scala R

ML/AI

TensorFlow PyTorch Scikit Learn Keras NLTK

Cloud & DevOps

GCP AWS Azure Docker Kubernetes

πŸ“ˆ GitHub Analytics

πŸ† Achievements

  • 🎯 Led development of credit risk models saving several $MM for clients
  • πŸš€ Successfully deployed ML models on various cloud platforms
  • πŸ“Š Developed decision engines resulting in $28MM annual savings

πŸ“š Latest Blog Posts

πŸ“Š Weekly Development Breakdown

Python        12 hrs 40 mins  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘  45.2%
ML/AI Tasks    8 hrs 15 mins  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘  29.4%
Cloud Dev      4 hrs 30 mins  β–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘  16.1%
Documentation  2 hrs 35 mins  β–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘   9.3%
# Life Philosophy
while alive:
    learn()
    code()
    innovate()
    repeat()

Pinned Loading

  1. Employee-Attrition Public

    Identifying whether an employee will leave the company based on an employee's attributes. I used ML algorithms such as Random Forest, Logistic Regression, Naive Bayes, and LightGBM

    Jupyter Notebook

  2. Kaggle-Nomad2018 Public

    I placed top 16% in the world on Kaggle in this competition. I used keras and tensorflow for deep learning and several ensembles with least correlated estimates to optimize for accuracy.

    Jupyter Notebook 1

  3. Predicting-Breast-Cancer Public

    Given a breast tumor's attributes, I used several machine learning models to predict whether if the tumor is malignant or benign

    Jupyter Notebook

  4. House-Prices Public

    I used Random Forest to predict a house prices given the house's attributes

    Jupyter Notebook